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On Bayesian model and variable selection using MCMC

On Bayesian model and variable selection using MCMC
On Bayesian model and variable selection using MCMC
Several MCMC methods have been proposed for estimating probabilities of models and associated 'model-averaged' posterior distributions in the presence of model uncertainty. We discuss, compare, develop and illustrate several of these methods, focussing on connections between them.
gibbs sampler, independence sampler, metropolis–hastings, reversible jump
0960-3174
27-36
Dellaportas, Petros
df8947f6-37ea-4e68-8967-eb43f777a5fd
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Ntzoufras, Ioannis
334f2302-b765-48d1-90b6-121ec8e31131
Dellaportas, Petros
df8947f6-37ea-4e68-8967-eb43f777a5fd
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Ntzoufras, Ioannis
334f2302-b765-48d1-90b6-121ec8e31131

Dellaportas, Petros, Forster, Jonathan J. and Ntzoufras, Ioannis (2002) On Bayesian model and variable selection using MCMC. Statistics and Computing, 12 (1), 27-36. (doi:10.1023/A:1013164120801).

Record type: Article

Abstract

Several MCMC methods have been proposed for estimating probabilities of models and associated 'model-averaged' posterior distributions in the presence of model uncertainty. We discuss, compare, develop and illustrate several of these methods, focussing on connections between them.

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More information

Published date: 2002
Keywords: gibbs sampler, independence sampler, metropolis–hastings, reversible jump
Organisations: Statistics

Identifiers

Local EPrints ID: 29965
URI: http://eprints.soton.ac.uk/id/eprint/29965
ISSN: 0960-3174
PURE UUID: df354b04-8013-4058-b3aa-ad8d4ccb8081
ORCID for Jonathan J. Forster: ORCID iD orcid.org/0000-0002-7867-3411

Catalogue record

Date deposited: 10 May 2006
Last modified: 18 Feb 2021 16:42

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